{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2a0b97d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp2d, interpn\n",
    "from numpy.matlib import repmat\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f6be88",
   "metadata": {},
   "source": [
    "# Entry/Exit with Homogeneous firms\n",
    "## and translog demand\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "244c912c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sequence_jacobian import solved, simple, create_model\n",
    "\n",
    "@simple\n",
    "def household(w, psi, nu):\n",
    "    L = (w/psi)**nu\n",
    "    return L\n",
    "\n",
    "@solved(unknowns={'v': 1.0, 'N': 1.0}, targets=['valfunc', 'accumulation'])\n",
    "def firms(C, sigma, v, beta, delta, Ne, N, L):\n",
    "    mu      = 1 + 1/(sigma*N)\n",
    "#    y       = p**(-sigma)*C\n",
    "    ell     = L/N            # imposing labor market clearing\n",
    "    pi      = (1-1/mu)*(C/N)\n",
    "    valfunc = v - (pi + beta*(1-delta)*v(1))\n",
    "    accumulation =  N - (1-delta)*N(-1) - Ne\n",
    "    return mu, ell, pi, valfunc, accumulation\n",
    "    \n",
    "@simple \n",
    "def mkt_clearing(mu, N, v, fE, w, Z, C, L, pi, Ne, Nbar):\n",
    "    p       = mu*w/Z\n",
    "    rho     = math.e**(-0.5*(Nbar - N)/(2*Nbar*N))\n",
    "    goods_mkt = p - rho                   # as in BGM\n",
    "    free_entry = v - fE*w/Z\n",
    "    budget     = C - w*L - N*pi\n",
    "    return goods_mkt, free_entry, budget, rho\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a70a25",
   "metadata": {},
   "source": [
    "### Steady State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d3fbc144",
   "metadata": {},
   "outputs": [],
   "source": [
    "ces = create_model([household, mkt_clearing, firms], name=\"CES Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bb885ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.090909090909093"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calibration = {'beta':    0.96,\n",
    "               'psi':     1,\n",
    "               'fE':      1.0,\n",
    "                'nu': 2.0,\n",
    "                'delta':  0.11,\n",
    "                'sigma':  .35,\n",
    "                'w': 1,\n",
    "                'Z': 1.0,\n",
    "                'Ne': 1, \n",
    "                'C': .25, \n",
    "                'Nbar': 20}\n",
    "\n",
    "ss = ces.steady_state(calibration)\n",
    "ss['N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "283c6fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "w0    = .5\n",
    "Nbar0 = 1/(calibration['sigma']*(1/w0-1))\n",
    "Ne0   = calibration['delta']*Nbar0\n",
    "\n",
    "calibration['Ne'] = Ne0\n",
    "\n",
    "ss=ces.solve_steady_state(calibration, \n",
    "      unknowns={'Nbar': Nbar0, 'w': 1.0, 'Ne': Ne0}, \n",
    "      targets={'rho': 1.0, 'free_entry': 0.0, 'goods_mkt': 0.0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1c603fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02589696861769697"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss['budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "87cc0f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss=ces.solve_steady_state(calibration, \n",
    "      unknowns={'w':ss['w'], 'Nbar': ss['Nbar'], 'Ne': ss['Ne'], 'C': .05}, \n",
    "      targets={'free_entry': 0.0, 'goods_mkt': 0.0, 'budget': 0.0, 'rho': 1.0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "de0cff75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8167476367529263"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss['mu']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e52b213",
   "metadata": {},
   "source": [
    "# Shock to Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1d08066a",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 300\n",
    "dZ = -.03 * .685 ** np.arange(T)\n",
    "irf = {}\n",
    "irf = ces.solve_impulse_linear(ss, ['w', 'Ne', 'C'], ['free_entry', 'goods_mkt', 'budget'], {'Z': dZ})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c00e2675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfp = (irf['C'] + ss['C'])/(irf['L'] + ss['L'])\n",
    "fig, axes = plt.subplots(2, 3, figsize=(8, 4))\n",
    "ax = axes.flatten()\n",
    "\n",
    "ax[0].plot(100*((irf['N'][1:10] + ss['N'])/ss['N']-1))\n",
    "ax[0].axhline(0, color='gray', linestyle=':')\n",
    "ax[0].set_title('Mass of establishments')\n",
    "ax[0].set_ylabel('pct deviation from ss')\n",
    "\n",
    "ax[1].plot(100*((irf['mu'][1:10] + ss['mu'])/ss['mu']-1))\n",
    "ax[1].axhline(0, color='gray', linestyle=':')\n",
    "ax[1].set_title('Markup')\n",
    "\n",
    "ax[2].plot(100*((tfp[1:10])/tfp[-1]-1))\n",
    "ax[2].axhline(0, color='gray', linestyle=':')\n",
    "ax[2].set_title('TFP')\n",
    "\n",
    "ax[3].plot(100*((irf['L'][1:10] + ss['L'])/ss['L']-1))\n",
    "ax[3].axhline(0, color='gray', linestyle=':')\n",
    "ax[3].set_title('Employment')\n",
    "\n",
    "ax[4].plot(100*((irf['C'][1:10] + ss['C'])/ss['C']-1))\n",
    "ax[4].axhline(0, color='gray', linestyle=':')\n",
    "ax[4].set_title('Consumption')\n",
    "\n",
    "ax[5].plot(100*((irf['w'][1:10] + ss['w'])/ss['w']-1))\n",
    "ax[5].axhline(0, color='gray', linestyle=':')\n",
    "ax[5].set_title('Wage')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7c458959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f97861701f0>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(irf['L'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
